From Teaching to Implementing Inventory Management: Problems of Translation

Zanakis, Stelios H.; Austin, Larry M.; Nowading, David C.; Silver, Edward A.

doi:10.1287/inte.10.6.103

Cited by 31 publications

(17 citation statements)

References 6 publications

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“…Therefore, this example shows the advantage of a "glass box" methodology, like the rough set approach, in comparison to a "black box methodology", like discriminant analysis, MCDA, or neural networks. This feature can make acceptance of model results and implementation more likely by decision makers, who "would not accept a solution that they do not understand" (Zanakis et al 1980). …”

Section: Case Study Results Via the Rough Set Methodologymentioning

confidence: 99%

Global investing risk: a case study of knowledge assessment via rough sets

et al. 2009

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This paper presents an application of knowledge discovery via rough sets to a real life case study of global investing risk in 52 countries using 27 indicator variables. The aim is explanation of the classification of the countries according to financial risks assessed by Wall Street Journal international experts and knowledge discovery from data via decision rule mining, rather than prediction; i.e. to capture the explicit or implicit knowledge or policy of international financial experts, rather than to predict the actual classifications. Suggestions are made about the most significant attributes for each risk class and country, as well as the minimal set of decision rules needed. Our results compared favorably with those from discriminant analysis and several variations of preference disaggregation MCDA procedures. The same approach could be adapted to other problems with missing data in data mining, knowledge extraction, and different multi-criteria decision problems, like sorting, choice and ranking.

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Section: Case Study Results Via the Rough Set Methodologymentioning

confidence: 99%

Global investing risk: a case study of knowledge assessment via rough sets

et al. 2009

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“…However, the practical implementation of inventory models is far behind the development of inventory modeling (Silver 1981). The discrepancy between theory and practice of inventory may come from both the academic and practical areas (Zanakis et al 1980). Much of the research is aimed at rigorous analysis of underlying equations representing inventory problems and developing mathematical decision models.…”

Section: Problem Statementmentioning

confidence: 99%

Computerised inventory management for a manufacturing industry: A case study in Nigeria

Owoeye

Adejuyigbe

Bolaji

et al. 2014

African Journal of Science, Technology, Innovation and Developm

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IntroductionWe all hold money, and one wonders if we all know the reasons we hold it. Economics objectively gave us three reasons why we keep money, and they are: the precautionary motive, the transactional motive and the speculative motive (Keynes 1923). Like money, companies keep inventory for these reasons stated above and even more. Inventory management is the act of keeping track of the quantity of material and number of item that should be and are presently in inventory at any time; supplies data required by other diameters of manufacturing cycle and links manufacturing to costing, book-keeping and general manufacturing. Inventory management, also often referred to as inventory control, can be referred to as the actual steps taken to maintain proper stock level in raw materials and finished goods (Adejuyigbe 2002).Over the years, inventory managers have been faced with the questions of how to monitor the inventory, how much should be ordered and when orders should be placed. This has led to the development of various models aimed at providing viable answers to the questions of inventory managers. The methodology for modelling inventory situations is straightforward and three simple steps are involved (Adam and Ebert 1982): 1. Examine the inventory situation carefully, listing characteristics and assumptions concerning the situation. 2. Develop the total annual relevant cost equation. 3. Optimise the cost equation to find the optimum for how much to order (order quantity) and when to reorder (reorder point).

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“…Some of the general issues have been previously addressed by other writers [e,g,, Evans, 1979;Ignizio, 1980;Silver, Vidal, and DeWerra, 1980;Woolsey, 1975b;Zanakis, 1977;Zanakis, Austin, Nowading, and Silver, 1980], Our emphasis here is to present a summary of practical guidelines on the use of heuristics for optimization. Thus we prefer the term "heuristic optimization," although the words seem inconsistent.…”

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Heuristic “Optimization”: Why, When, and How to Use It

Zanakis¹,

Evans²

1981

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119

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The pressing need of real-world problems for quick, simple, and implementable solutions coupled with a recently increased research productivity on improved and rigorously evaluated heuristic methods are rapidly increasing the need, usage, and respect for heuristics. This paper presents a framework for heuristic “optimization” by systematically examining this change in attitudes towards heuristics, their desirable features and proper usage.

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From Teaching to Implementing Inventory Management: Problems of Translation

Cited by 31 publications

References 6 publications

Global investing risk: a case study of knowledge assessment via rough sets

Global investing risk: a case study of knowledge assessment via rough sets

Computerised inventory management for a manufacturing industry: A case study in Nigeria

Heuristic “Optimization”: Why, When, and How to Use It

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